Vanderbilt University Law School Law and Economics

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1 Vanderbilt University Law School Law and Economics Resarch Paper Number Opting Out among Women with Elite Education Joni Hersch Vanderbilt University This paper can be downloaded without charge from the Social Science Research Network Electronic Paper Collection:

2 Opting Out among Women with Elite Education Joni Hersch Professor of Law and Economics Vanderbilt Law School st Avenue South Nashville TN phone fax March 26, 2013 Abstract Whether highly educated women are exiting the labor force to care for their children has generated a great deal of media attention, even though academic studies find little evidence of opting out. This paper shows that female graduates of elite institutions have lower labor market involvement than their counterparts from less selective institutions. Although elite graduates are more likely to earn advanced degrees, marry at later ages, and have higher expected earnings, there is little difference in labor market activity by college selectivity among women without children and women who are not married. But the presence of children is associated with far lower labor market activity among married elite graduates. Most women eventually marry and have children, and the net effect is that labor market activity is on average lower among elite graduates than among those from less selective institutions. The largest gap in labor market activity between graduates of elite institutions and less selective institutions is among MBAs, with married mothers who are graduates of elite institutions 30 percentage points less likely to be employed full-time than graduates of less selective institutions. JEL Classifications: I21, J16, J22 Acknowledgements: I thank Alison Del Rossi, Shoshana Grossbard, and Bruce Weinberg for helpful comments.

3 Opting Out among Women with Elite Education I. Introduction The large increase in married women s labor force participation over the past four decades and their sustained high labor force participation would seemingly put to rest any lingering doubts about women s commitment to the labor force. Yet a leveling-off of married women s labor force participation rate in the 1990s, followed by a slight downturn beginning in the late 1990s, has raised new questions about the strength of women s labor force attachment. 1 The possibility that highly educated women are reducing their labor market activity or exiting the labor force to care for their children at higher rates than their less educated peers, referred to as opting out, has received extensive media attention and has generated a great deal of controversy (e.g., Belkin 2003, Story 2005). Academic studies largely find little evidence that opting out is an important phenomenon. 2 But these studies provide no information on whether opting out differs by college selectivity, and, as highlighted in the media and noted by Goldin (2006) and Goldin and Katz (2008), the discussion of opting out is generally interpreted to refer to female graduates of highly selective institutions. In this paper, I show that graduates of elite institutions have lower labor market activity than graduates of less selective institutions. Because the majority of women are not graduates of elite institutions, there may be little overall evidence of opting out even though graduates of elite institutions are indeed opting out. 1 This trend has been widely reported and analyzed. See for instance Juhn and Potter (2006) and Macunovich (2010). 2 Many papers examine the opting out hypothesis by comparing women s employment rates by cohort, education, presence of children, or occupation and generally find little evidence of a trend in labor force exit of more educated mothers (e.g., Boushey 2005, 2008; Goldin 2006; Percheski 2008; Antecol 2011; Hotchkiss, Pitts, and Walker 2011). Shang and Weinberg (2013) do not examine the opting out hypothesis directly but find a trend of higher fertility among more educated women, which could be consistent with greater opting out of more educated women. 1

4 The principal challenge in addressing this question is availability of data on a sufficiently large sample of women that includes labor market information as well as information on selectivity of college institution, such as whether it should be categorized as elite. Studies that investigate the possibility of opting out among women in general have used data that do not provide information on quality or selectivity of educational institution (e.g., Boushey 2005, 2008; Percheski 2008; Kreider and Elliott 2009; Antecol 2011), or else consider only graduates of elite institutions. Goldin (2006) examines graduates of the 34 selective schools included in the College & Beyond data set; Bertrand, Goldin and Katz (2010) examine MBA graduates of University of Chicago; and Goldin and Katz (2008) and Herr and Wolfram (2012) examine Harvard graduates. Thus, despite the focus on graduates of elite institutions, there are no studies that compare the labor market activity of elite graduates to non-elite graduates. 3 Furthermore, the studies of elite graduates listed above that conclude there is no trend in opting out among elite graduates have limitations. The College & Beyond data analyzed in Goldin (2006) reflects out-of-work spells retrospectively reported in from the entering class of 1976, and therefore predates the time period in which concerns over opting out arose. Goldin and Katz (2008) is based on respondents to the 2006 Harvard and Beyond survey (comprised of three cohorts of Harvard/Radcliffe graduates in periods around 1970, 1980, 1990) which requested retrospective reports of out-of-work spells of 6 months or more. Although the 2006 survey period coincides with the time period in which concerns over opting out arose, the survey had an overall response 3 Notably, the highly publicized articles in the media assert that opting out is a trend, but do not provide any empirical support of a trend. Specifically, Belkin s 2003 New York Times Magazine cover article, in which she coined the term opt-out revolution, profiles a group of female Princeton graduates and MBAs who left successful careers in order to care for their children, and Story (2005) profiles students at Yale. 2

5 rate of 40 percent and a response rate for women of 45.7 percent. Bertrand, Goldin, and Katz (2010) is based on respondents to a survey conducted in of University of Chicago Booth School of Business MBA graduating classes of 1990 to 2006, which also requested information on out-of-work spells of 6 months or more and had a response rate of 31 percent among those with known addresses. The response rates in the latter two surveys are low enough to raise concerns about possible response biases that might conceal actual trends in opting out, especially if women selectively respond on the basis of their labor market status. 4 I examine whether labor market activity is related to college selectivity using data from the 2003 National Survey of College Graduates (NSCG). The 2003 NSCG provides detailed information for more than 100,000 college graduates across the full universe of colleges and universities, including a large share who are married mothers and who have graduate degrees. To identify college selectivity, I use information on Carnegie classification 5 available in the 2003 NSCG to group institutions into selectivity tiers by comparing by name schools in each Carnegie classification to schools in selectivity categories in Barron s Profiles of American Colleges. 6 Although the NSCG data are cross-sectional and do not allow me to examine trends over time, this analysis provides information on whether opting out is a more important phenomenon among the elite graduates profiled in the media than among the majority of the population who are not elite graduates, and therefore provides information on whether the limited evidence of opting out shown so far may relate to the small share of the population comprised of elite graduates. 4 Herr and Wolfram (2012) do not examine trends in opting out over time but compare employment rates by highest degree using data from the Harvard graduating classes of who responded to the 10th and 15th anniversary reports. They report a response rate for women of 55 percent. 5 Carnegie Foundation for the Advancement of Teaching (c1994). 6 Barron s Educational Series, Inc. (1994). 3

6 Although graduates of elite institutions marry later, are more likely to earn graduate degrees, and have higher expected earnings, the labor market activity of elite graduates with children is substantially lower than that of elite graduates without children, as well as substantially lower than that of graduates of less selective institutions. Most women eventually marry and have children, and the net effect is that labor market activity is on average lower among elite graduates than among those from less selective institutions. For example, the employment rate is 78 percent among female graduates of the most selective institutions who are between ages 23 and 54 and is 84 percent among comparable female graduates of less selective institutions. The primary difference in labor market activity by selectivity of institution arises among women who are married and have children ages 18 or younger. The employment rate for married mothers with children who are graduates of the most selective colleges is 68 percent, in contrast to an employment rate of 76 percent of those who are graduates of less selective colleges. Other measures of labor market activity such as labor force participation, full-time employment, parttime employment, and employment in two periods 18 months apart show similarly large disparities in labor market activity by college status. Furthermore, the disparity in labor market activity between graduates of elite colleges and less selective colleges is observed for women in three different age cohorts: ages 23-34, 35-44, and Controls for detailed educational background, graduate degrees, current or previous occupation, number and age of children, spouse s characteristics, and family background reduce but do not usually eliminate the disparity in labor market activity. The largest gap in labor market activity between graduates of elite versus non-elite schools occurs among those with MBAs. For example, married MBA mothers with a bachelor s degree from the most selective schools are 30 percentage points less likely to be employed full-time 4

7 than among those with a bachelor s degree from a less selective school, controlling for selectivity of their MBA institution, current or prior occupation, spouse s characteristics, number and age of children, and family background. This paper shows that married mothers who are graduates of elite colleges have lower labor market activity rates than their counterparts from less selective institutions. 7 Labor market exits among highly educated mothers are often interpreted as a response to inflexible workplaces that make combining family and career incompatible. But if inflexible workplaces are a primary cause of lesser labor market activity of mothers, labor market activity should not differ by college selectivity, or may even favor elite graduates. Other factors that may differ between elite and non-elite graduates, such as heterogeneity in preferences about market work, may be more influential determinants of labor market activity than inflexible workplaces. II. Empirical motivation The objective of this paper is to identify empirically whether female graduates of elite institutions differ in their labor market activity compared to graduates of less selective institutions. I estimate labor market activity equations of the following general form: (1) Prob(E = 1) = α 0 + α 1 W + α 2 S + α 3 Y + α 4 C + Qγ + Xβ + ε where E is an indicator variable representing the individual s labor market activity (six measures are examined in this paper); W represents the individual s expected wage offer; S represents spouse s earnings (if married); Y represents nonlabor income, including parents income or wealth; C represents the number of children; Q is a vector of indicators for the quality of the 7 Because there are no earlier data that would allow examination of trends, this paper does not provide direct evidence on whether the lower labor market activity of elite graduates relative to graduates of less selective institutions identified in this paper represents a change over time. 5

8 individual s educational institution; X is a vector of control variables; α 0, α 1, α 2, α 3, α 4, γ and β are parameters to be estimated; and ε is the random disturbance term. Equation (1) indicates that labor market activity is determined by own expected wage offer W, nonlabor income S and Y, and the presence of children C. Estimates of conventional labor supply equations show that labor force participation is positively related to expected wage offers, negatively related to nonlabor income, and, for women, negatively related to the presence of children. As discussed below, in addition to these direct effects on labor market activity, the quality of the individual s educational institution is expected to be related to W, S, Y, and C. Thus, γ measures the effect of graduation from colleges of different selectivity on labor market activity net of the indirect relation between college selectivity and W, S, Y, and C. If the full influence of college selectivity on labor market activity is captured through the relation between selectivity and W, S, Y, and C, then E(γ) = 0, even though unadjusted means may show significant differences in labor market activity by college selectivity. The expected relations between college selectivity and W, S, Y, and C are as follows. First, higher-ability students sort into higher-quality educational institutions and are more likely to earn advanced degrees, and graduates of higher-status schools and those with advanced degrees have higher (or at least no lower) expected wages (Brewer, Eide, and Ehrenberg 1999; Monks 2000; Black, Daniel and Smith 2005; Dale and Krueger 2002, 2011). Because the empirical analysis examines labor supply at the extensive margin, higher expected wages unambiguously should increase higher labor market activity among those from more selective colleges. 8 8 The effect of higher wages on hours worked conditional on the decision to work positive hours (that is, the intensive margin) is ambiguous as income and substitution effects have opposite effects on hours worked. 6

9 Second, spousal income may be related to institutional selectivity. Not only are women likely to marry men with similar levels of education (e.g., Schwartz and Mare 2005), but they are also likely to marry men with similar parental wealth (Charles, Hurst and Killewald 2013) and who graduate from colleges of similar status (Arum, Roksa and Budig 2008). Such assortative marriages may decrease labor supply of women from more selective schools, as their spouses will bring to the marriage greater nonlabor assets as well as higher expected labor income. However, the effect of assortative marriages on labor supply may be small, as women s labor supply has become less responsive to their husbands wages over the period (Blau and Kahn 2007). Third, family background differences in financial status and culture influence the costs of higher education. In an intergenerational model with imperfect capital markets in which parents decide between their own consumption and investment in their children, wealthier parents can invest in their children both financially and through transmission of culture or networks, while lower income parents shift costs of higher education to their children (Becker and Tomes 1986). Thus children of lower income parents are more likely to attend less expensive public universities. They are also more likely to be responsible for student debt incurred to finance their education. There is an inverse relation between students family income and the likelihood of borrowing to finance undergraduate education. 9 Graduates of elite institutions are likely to carry less debt, which would tend to decrease their labor supply. 9 See U.S. Department of Education, National Center for Education Statistics (2005). For example, data from the Baccalaureate and Beyond Longitudinal Studies show that in , which is close to the average graduation year within the sample examined in this paper of 1989, the percent who borrowed for their undergraduate education by family income quintile is as follows: lowest, 66.7; lower middle, 45.1; upper middle, 34.3; highest, See 7

10 Fourth, it is widely established that female labor supply is negatively related to the presence of children. The timing of childbearing may lead to cohort differences in labor supply by selectivity of college. Because elite graduates are more likely to earn advanced degrees, to the extent that childbearing is deferred until all education is completed, labor supply may be higher among elite graduates within younger cohorts. A related explanation of the relation between labor supply of elite graduates and children draws on Grossbard-Shechtman s model of work-inhousehold (1984, 1993). Because elite women on average have children later than non-elite graduates, those who are willing to have children (or have them at a younger age) may receive higher quasi-wages within marriage. This would imply that women with children who are graduates of elite institutions may be less likely to participate in the labor market as a result of the income effect generated from their higher quasi-wages. Finally, in addition to the relation between college selectivity and wages, spouses income, family background, and children, college selectivity may be a proxy for preferences. There may be greater heterogeneity in preferences for labor market activity among graduates of elite institutions than among graduates of less selective institutions, for reasons similar to that addressed by Brand and Xie (2010). Cultural and social norms make it more likely that students from more financially or educationally privileged backgrounds attend college regardless of their preferences for labor market activity, whereas students from less privileged backgrounds are likely to attend college only if their expected economic payoff, which will depend on their lifetime labor market activity, exceeds the cost. Preferences for market work may therefore be more heterogeneous as well as on average lower among elite graduates to the extent that students from more privileged backgrounds are more likely to attend higher-status colleges even after taking into account their academic qualifications (Hearn 1984). 8

11 III. National Survey of College Graduates To identify whether graduates of elite and less selective institutions differ in their labor market activity, I use data from the 2003 National Survey of College Graduates (NSCG). This survey is conducted by the U.S. Bureau of the Census for the National Science Foundation and is part of an ongoing National Science Foundation data collection program known as SESTAT (Scientists and Engineers Statistical Data System). 10 The 2003 NSCG surveyed 100,402 individuals who held a bachelor s degree or higher prior to April 1, 2000 and were ages 75 or younger as of the survey reference date of October 1, 2003 (with eligibility identified from the 2000 Decennial Census long form). The requirement for sample inclusion of a bachelor s or higher degree by April 1, 2000 resulted in a minimum age in the sample of 23, with only six observations under age 25. The survey design is based on a stratified random sampling frame, so I use the NSCG sampling weights to control for the sample design in the reported descriptive statistics and regressions. 11 The NSCG provides information on several measures of labor supply. Respondents report whether they were working for pay or profit in the survey reference week, if not employed whether they looked for work in the preceding four weeks, whether they usually worked a total of 35 or more hours per week on all s held during the reference week, and the actual number of hours worked during a typical week on their principal. Using this information I construct 10 See for information about the SESTAT surveys and for access to data and questionnaires. Except for the 1993 and 2003 surveys, the NSCG is limited to science and engineering degree holders or those in science and engineering occupations. The 1993 and 2003 NSCG surveys are special baseline surveys that include college graduates with degrees in any field. 11 The weighted response rate is 73 percent, with the sampling weights designed to reflect differential selection probabilities on the basis of demographics, highest degree, occupation, and sex, and adjusted to compensate for nonresponse and undercoverage of smaller groups. See 9

12 indicators for employment, labor force participation, full-time employment on principal, parttime employment on principal, and full-time employment counting all s. Some studies examine full-time, full-year employment, where full-year employment is identified as working 50 weeks or more in the preceding year. Although the NSCG reports hours typically worked per week, it does not report weeks worked. However, in addition to employment status in the reference week, the survey does report employment status on April 1, To analyze the greater labor force attachment implied by full-time, full-year employment relative to the single point in time measures, I construct an indicator variable for those who were employed full-time on their principal in the survey reference week and also employed on April 1, 2001 (the survey does not report whether the April 1, 2001 employment is full-time). Thus, I examine six dichotomous labor status outcomes. The NSCG has highly detailed information on educational attainment. Respondents provide information on their first bachelor s degree and up to two additional degrees at the bachelor s level or higher. For each degree, respondents report the year the degree was awarded, type of degree (bachelor s, master s, doctorate, or professional degree), and field of study. The field of study is selected from a list of over 140 educational fields. I record the field of the first bachelor s degree in eight groups: Arts/Humanities; Business/Economics; Education; Engineering; Math/Computer Science; Science; Social Science; and other fields such as architecture, social work, communications, journalism, home economics, or library science. Using information on type of highest degree combined with educational field, I also create eight mutually exclusive categories for those with highest degree PhD, MD, JD, MBA, MA in education, master s in a field other than education or business ( MA other ), other professional 10

13 degree, and highest degree bachelor s ( BA highest degree ). 12 I create an additional indicator variable for full-time students (in addition to the attained degrees indicated above). Respondents report their current occupation (if employed) or last occupation by selecting the code from a list of 128 categories provided in the survey. I group these occupations into nine broader categories based on the 2010 SOC intermediate aggregation level categories as follows: Management, Business, Financial; Computer, Engineering, Science; Education, Legal, Community Service, Arts, Media; Healthcare Practitioners and Technical; Service; Sales and Related; Office and Administrative Support; and Natural Resources, Construction, Maintenance, Production, Transportation, Material Moving (referred to as traditional blue-collar ), with a final category for occupations not reported. Available information on demographics and family background includes the following. Respondents report their sex, age, marital status, whether their ethnicity is Hispanic or Latino, race, country of citizenship, and location of their residence or of their principal employer, which is recorded in nine Census categories. I construct indicator variables for four racial categories of white, black, Asian, and all other races or multiple races, for native born US citizens, and for four regions of Northeast, Midwest, South, and West. 13 Respondents report the number of children under age 2, ages 2-5, ages 6-11, and ages living with the respondent as part of their family at least 50 percent of the time. I combine those 12 Specifically, the highest degree for those with a professional degree in the field of law/prelaw/legal studies are categorized as JDs; those with a professional degree in the field of medicine (which includes dentistry, optometry, and osteopathy) are categorized as MDs; those with highest degree master s in a business field such as accounting, business administration, financial management, and marketing are categorized as MBAs; those with highest degree master s in an education field such as mathematics education, education administration, secondary teacher education, and so forth are categorized as MA education. All remaining degrees are grouped into other professional degrees, MA other, or highest degree bachelor s. 13 Of the full sample of 100,402 observations, there are 76 respondents who report a non-us location. I group these with the omitted category in the regressions. 11

14 living in a marriage-like relationship with those who are married, as the survey requests the same information about spouses for both groups, and refer to these respondents as married. The NSCG does not provide information on spouse s earnings nor on spouse s actual education. As a proxy for spouse s earnings, I use information on whether the spouse is employed, and among those employed, whether the spouse s duties on their requires the technical expertise of a bachelor s degree or higher (with multiple responses permitted) in engineering, computer science, math, or the natural sciences (referred to as S&E ); in the social sciences; in some other field such as health, business, or education; or does not require expertise at the level of a bachelor s degree or higher. Thus, these measures combine an indicator of employment status with information on education and occupation. 14 Measures of family background include parents education and location where the respondent attended high school. Father s and mother s education levels are recorded in seven categories from less than high school completed to doctorate. Charles, Hurst and Killewald (2013) find that parents education is a strong predictor of their reported wealth. The majority of individuals who attend college were from the state where they enrolled. 15 The concentration of colleges and their quality varies regionally, with a large concentration of selective colleges in the Northeast. The cost of attending a selective college is expected to be higher for those living in other regions of the country. Thus high school location provides a rough proxy for the costs of attending college. High school location is recorded in nine Census regions for those attending 14 This NSCG measure that combines education and occupational information mitigates possible endogeneity of spouse s earnings with own labor supply and also provides a measure reflecting permanent income or longer-term earnings. Any findings may be similar, however, if controlling instead for spouse s earnings. Bertrand, Goldin and Katz (2010) report that in their analysis of labor supply of female MBAs their qualitative findings are similar when controlling either for spouse s earnings category in the survey year or for spouse s relative education level. 15 See College Board, Trends in College Pricing 2012, Figure 24A, available at 12

15 high school in the US or by country for those outside the US. I group into 5 categories: Northeast, Midwest, West, South, and not US. IV. Identifying college selectivity Years of education and highest degree attained are reported in many data sets that are used to examine labor market outcomes, but there is considerably less information available on the quality of colleges attended. Although the 2003 NSCG does not report specific information on school quality or names of the colleges or universities of the graduates in the sample, it does reports the 1994 Carnegie classification of the college or university awarding each degree reported by respondents. 16 Whether the institution is private or public is also indicated. The Carnegie classifications are not intended to be used as a measure of institutional selectivity, but as I show, in conjunction with other ratings, there is a high correspondence between Carnegie classification and selectivity. I therefore use the 2003 NSCG because it provides a much larger sample and a broader age range than available in other data sets reporting college information, as well as a far larger sample of married mothers in the age range and time period relevant for consideration of the opting out hypothesis. 17 There are 3,595 institutions classified in the 1994 revision of the Carnegie classification system. Institutions outside of the US are not classified. The highest degree awarded in Carnegie classification is not reported in the 1993 NSCG. 17 Data sets that report college information include the National Longitudinal Study of the High School Class of 1972 (NLS72), High School and Beyond (HS&B), National Longitudinal Survey of Youth 1979 (NLSY79), and the 1975 and 1976 waves of the Panel Study of Income Dynamics (PSID). The sample of married mothers using the NLS72, HS&B, NLSY79 or PSID would be far smaller than available in the NSCG. Brewer, Eide, and Ehrenberg (1999) report a maximum of 3,062 observations with any college attendance (whether or not they graduated) in the NLS72 and a maximum of 2,165 similar observations in the 1982 HS&B cohort, and they note that their sample sizes are reduced dramatically if restricted to college graduates. Monks (2000) reports a sample of 1,087 college graduates in the NLSY79 that he was able to match to Barron s categories. Arum, Roksa, and Budig (2008) report a sample of 293 married female college graduates ages in the 1975 wave of the PSID. 13

16 percent of these institutions is the associate of arts degree, and respondents with their highest degree from such an institution would generally not be included in the NSCG. 18 Another 20 percent of institutions are classified as specialized institutions. 19 The remaining 39 percent of institutions are classified into 8 categories: Research I, Research II, Doctoral I, Doctoral II, Master s I, Master s II, Liberal Arts I, and Liberal Arts II. The 1994 classifications are based on factors such as the highest degree awarded; the number, type, and field diversity of post-baccalaureate degrees awarded annually; and federal research support. For example, Research I universities offer a full range of baccalaureate programs through the doctorate, give high priority to research, award 50 or more doctoral degrees each year, and receive annually $40 million or more in federal support. Although the Carnegie classifications are not intended to correspond to measures of selectivity, selectivity is explicitly taken into account in classifying liberal arts colleges into Liberal Arts I or Liberal Arts II. A frequently cited classification of college selectivity is Barron s Profile of American Colleges. Based on quality indicators of the entering class (SAT or ACT, high school GPA and high school class rank, and percent of applicants accepted), Barron s 18 Some two-year colleges offer bachelor s degrees in some majors or cooperatively with another four-year institution. I group the handful of respondents with bachelor s degrees from associates of arts institutions with specialized institutions. 19 These are free-standing campuses and include professional schools that offer most of their degrees in one area (e.g., medical centers, law schools, schools of art, music, and design, and theological seminaries). Among the specialized institutions are highly selective universities such as the United States Air Force Academy and the United States Naval Academy and institutions such as The Naropa Institute in Boulder, Colorado, that are difficult to classify. See and Carnegie Foundation for the Advancement of Teaching (c1994). 14

17 places colleges into seven categories: most competitive, highly competitive, very competitive, competitive, less competitive, noncompetitive, and special. 20 I group Carnegie classifications into a smaller number of categories by reference to Barron s ratings of selectivity. To identify the relation between Carnegie classification and Barron s rating of selectivity in the same year (1994), I compared by institution name the list of schools in each Carnegie classification to the list of schools rated by Barron s as either most competitive or highly competitive. Table 1 reports the number of such schools in each Carnegie classification category by public or private institutional control. For example, there are 29 private Research I universities, with 16 rated most competitive and 7 rated highly competitive. Of the 11 private Research II universities, 9 are rated as either most competitive or highly competitive. In contrast, there are 59 public Research I universities, with only one rated as most competitive and 10 rated as highly competitive. The vast majority of Liberal Arts I colleges are private. Of the 7 public Liberal Arts I colleges, none are rated as most competitive and only one is rated as highly competitive. Of the 159 private Liberal Arts I colleges, 54 are rated as most competitive or highly competitive. Based on this relation between Carnegie classification and Barron s selectivity rating, I stratify the set of colleges by Carnegie classifications so that the share of schools rated by Barron s as most or highly competitive is significantly different between groups. 21 This 20 Barron s categorizes professional schools of arts, music, and theater arts as special. Barron s special schools are therefore a subset of institutions classified as specialized in the Carnegie classification system. 21 Specifically, tests for differences in proportions are as follows: private Research I and private Research II compared to private Liberal Arts I yields z=5.25 (e.g., comparing 32/40 to 54/159); private Research I and private Research II compared to public Research I yields z=6.04; public Research I and private Liberal Arts I yields z=2.20; public Research I to all categories except private Research I, private Research II, and private Liberal Arts I yields z=8.16. Differences between other categories are not statistically significant. My grouping differs from that of 15

18 procedure results in four groups: private Research I and private Research II universities (referred to as tier 1 ); private Liberal Arts I colleges ( tier 2 ); public Research I universities ( tier 3 ); and colleges in the remaining Carnegie classifications, excluding specialized institutions ( tier 4 ). I also create indicator variables for specialized institutions, non-us institutions, and a final category for those graduating from US institutions that are missing information on Carnegie classification. For those with graduate degrees, the tier associated with their bachelor s degree may differ from the tier of their highest degree. For example, most private Liberal Arts I colleges (tier 2) offer few (if any) professional degrees, which means most of those with both a bachelor s degree from a liberal arts college and a professional degree will be assigned different tiers for their undergraduate and highest degrees. In addition to assigning tier based on bachelor s degree, I also assign to each individual the tier associated with their highest degree and the tier associated with the more selective of their bachelor s degree tier and highest degree tier. Because graduates of elite institutions are more likely to have advanced degrees than are graduates of less selective institutions, and because status of bachelor s degree institution is influential in determining admission to and success in highly ranked professional and graduate programs, use of the latter two tier measures implies that the comparison across tiers will disproportionately compare those with elite graduate degrees to those with less selective bachelor s degrees. Using bachelor s degree tier allows a comparison of individuals at a similar Bertrand, Goldin and Katz (2010, fn 2) who classify as selective those institutions with Carnegie classifications of Research I and Liberal Arts I regardless of public or private status. Separating private and public colleges into different tiers is also supported by the findings of Brewer, Eide and Ehrenberg (1999) on the relation between college status and earnings. Their study shows a large premium to attending an elite private college and a smaller premium to attending a middle-rated private college, with little support for a premium to higher-quality public institutions. 16

19 stage of their education. However, because bachelor s degree rank is the same as highest degree rank for 84 percent of the sample examined in this paper, the results are similar regardless of tier measure used. Furthermore, estimates stratified by highest degree reported in section VIII control for tier of bachelor s degree as well as tier of graduate or professional degree. V. Labor market activity descriptive statistics by tier Table 2 provides descriptive statistics by tier on labor market activity based on all women in the NSCG sample who are ages as of the survey year of This yields a sample of 33,307 women. The upper age of 54 is chosen to consider women who would have been born since around 1949 (actual birth year is not reported although age is reported) and would largely have earned their bachelor s degrees in or later than the early 1970s. As Goldin (2006) discusses in depth, a quiet revolution began around 1970 with an abrupt change in women s expectations about their future work lives, which was accompanied by increases in college attendance and graduation, reduced concentration in traditionally female majors, and increased professional and graduate school enrollment. The rapid increase in female labor force participation around the 1970s also relates to sex role ratios that were less favorable to women who were born during the baby boom era (Grossbard and Amuedo-Dorantes 2007). For each categorization of tier (bachelor s degree, highest degree, or more selective of bachelor s or highest degree), Table 2 reports rates for the six measures of labor market activity defined in section III: employment, labor force participation, full-time in principal, part-time in principal, 22 full-time counting all s, and employed full-time in the survey reference week (October 1, 2003) as well as employed on April 1, The statistics in this table do not adjust for any individual characteristics and are provided to illustrate differences by tier in labor 22 The pattern shown for part-time employment is similar if calculated conditional on employment. 17

20 market activity. Graduates of institutions without a Carnegie classification or of specialized institutions are included in the calculation of the overall labor market activity rates reported in the first column of numbers but their rates are not separately reported. Tests for significant differences between tiers based on a Bonferroni multiple comparison test are reported in the last column. As Table 2 shows, there are considerable differences in all measures of labor market activity by tier that consistently show greater labor market activity of graduates of less selective institutions. Consider for example Panel A which reports labor market rates based on tier of bachelor s institution. The difference in labor market activity rates between tier 1 graduates and tier 4 graduates ranges from 5.3 percentage points for current employment to 9.4 percentage points for employment in 2001 and full-time in 2003, and tier 1 graduates are 2.4 percentage points more likely to be employed part-time in their principal than tier 4 graduates. The disparities by tier are somewhat smaller when the comparison is by tier of highest degree (Panel B) or tier of the more selective of bachelor s degree and highest degree (Panel C), but the pattern and statistical significance of differences by tier in labor market activity remain The NSCG includes students on paid work-study as working for pay or profit. I chose to include full-time students in the analyses that are not stratified by highest degree and to exclude full-time students in the analyses by highest degree. No results of this paper are affected by whether students are included or excluded from any of the analyses, in part because the sample design results in relatively few students in the sample and in part because the employment rate for students is similar to that of nonstudents, as many students are employed (not only in workstudy s). For example, excluding full-time students from the calculations results in only two differences in the statistical significance of differences by tier relative to those reported in Panel A of Table 2: the difference in employment rates between tier 1 and tier 2 graduates becomes statistically significant at the 5 percent level; and the difference in part-time employment between tier 1 and tier 4 graduates is no longer significant at the 5 percent level. The reason for my choice of treatment of full-time students is as follows. First, because graduates of elite institutions are more likely to earn post-baccalaureate degrees, excluding students would raise the concern that the gap in labor market activity by college selectivity is driven by a combination of higher graduate school enrollment among elite bachelor s degree graduates accompanied by 18

21 VI. Background characteristics by tier As Table 2 demonstrates, women who are graduates of elite institutions have on average lower labor market activity than graduates of less selective institutions. The following sections examine why elite graduates have lower labor market activity by considering the roles of demographics, cohort, college major, graduate and professional degrees, occupation, marital status, number of children and their mix of ages, family background, and among those who are married, spouse s employment characteristics. Table 3 summarizes information on selected individual characteristics by tier of bachelor s degree. 24 This table reports the percentages of graduates with highest degree doctorate, professional, or MBA, with fathers and mothers with bachelor s degree or higher, with fathers and mothers with PhD or professional degrees, 25 and high school attendance in the Northeast. The NSCG does not report age of marriage or age of childbearing, so I report instead the proportion of women within each age group who are married and who have children living with them at least 50 percent of the time stratified into three age cohorts: 23-34, 35-44, and For those who are married, the table reports the percentages of spouses who are employed and the percentages of employed spouses in s requiring a bachelor s degree or higher in any field as well as in the three specified areas of science and engineering, social sciences, or other fields. lower labor market activity while a student. Second, although setting a minimum age for the sample at which it is expected that schooling is completed might mitigate the first concern, examination of the distribution of ages of full-time students as well as the distribution of ages at which the highest degree is attained shows that the age range of students and age at highest degree attainment is broad, and any choice of minimum age would be arbitrary. Third, however, I do exclude students from the analyses by highest degree attained in order to examine the relation between labor market activity and highest degree attained, while controlling for selectivity of highest degree in addition to selectivity of bachelor s degree. 24 Descriptive statistics for the full sample and by tier for all variables defined in section III and used in the regressions that follow are provided in Appendix A. 25 MBAs are reported as master s degrees and are not included with professional degrees. 19

22 As Table 3 shows, there are substantial differences in educational attainment by tier. The share of graduates with highest degree PhD, professional, or MBA is 26 percent among tier 1 graduates and drops steadily across tiers, with only 7 percent of tier 4 graduates having highest degree PhD, professional, or MBA. 26 Differences in parents education by tier are likewise substantial. For example, 64 percent of fathers of tier 1 graduates and 63 percent of fathers of tier 2 graduates have at least a bachelor s degree, in contrast to 50 percent of fathers of tier 3 graduates and 34 percent of fathers of tier 4 graduates. There is a similar pattern of educational attainment for mothers. Half of mothers of tier 1 and tier 2 graduates have at least a bachelor s degree, in contrast to one-quarter of mothers of tier 4 graduates. The largest differences in characteristics are between those who graduated from colleges in tier 1 compared to those who graduated from colleges in tier 4, with tier 3 characteristics falling between tier 1 and tier 4. Relative to graduates of less selective institutions, graduates of elite institutions on average marry and have children later, although the differences in marital status and presence of children ages 18 or younger observed in the youngest age group narrow considerably for those in age groups and For example, of tier 1 graduates in the age group, 61 percent are married and 29 percent have children. In contrast, among tier 4 graduates in this age group, 72 percent are married and 51 percent have children. However, in the age group, about 80 percent are married with little difference by tier, and about 73 percent have at least one child in the household ages 18 or younger with no statistical difference by tier. There are no statistically significant differences in the share married in the age group. However, within this age 26 With the exception of graduates of private Liberal Arts I colleges (tier 2), there is little difference within tiers by cohort with highest degree PhD, professional, or MBA. The share of tier 2 graduates with these degrees increases from 13 percent among the age cohort to 20 percent in both of the older cohorts. 20

23 group, those in tiers 3 and 4 are less likely to have children in the household ages 18 or younger reflecting their younger age at childbearing. Among those who are married, there is little difference in the percentage of spouses who are employed. But the educational requirements of s held by spouses differ between tiers, although by less than parents education, reflecting the tendency of college-educated women to marry similarly-educated men. For example, among those whose spouses are employed, the spouses of 79 percent of tier 1 graduates are employed in s requiring a bachelor s degree or higher (in contrast to 61 percent among tier 4 graduates), and these spouses are more likely to be in s that require expertise in higher-paying science and engineering fields as well as in other fields which includes expertise in health and business. Although there are some differences by tier among not-married women and childless women (which will be discussed later), women s labor market activity is primarily associated with the presence of children, and, as indicated in Table 3, the timing of marriage and presence of children differs across tiers. Table 4 provides background statistics on labor market activity for married women by tier, cohort, and presence of children ages 18 or younger. For each labor market activity, the first row refers to all married women ages 23-54; the row headed has children refers to married women ages with children; and the following three rows refer to married women in the indicated age group with children. Similarly, the row headed no children refers to married women without children and the following three rows refer to married women in the indicated age group without children. The relation between the presence of children and labor market activity is considerable and varies by cohort and tier. For example, compare employment rates within tier between married women with and without children. For tier 1 graduates, women without children in age groups 21

24 23-34 and are 27 percentage points and those in age group are 12 percentage points more likely to be employed than their counterparts with children. Among tier 4 graduates, the corresponding differences between employment rates of women with and without children are smaller at 21 percentage points for ages 23-34, 16 percentage points for ages 35-44, and 5 percentage points for ages Comparing labor market activity rates across tiers by cohort and presence of children suggests that much of the differences by tier identified in Table 2 is associated with the presence of children, and also that the pattern reported in Table 2 of lower labor market activity of elite graduates is not simply a cohort effect. For example, Panel A of Table 2 shows that on average, tier 4 graduates are 5.3 percentage points more likely to be employed than tier 1 graduates. But the difference by tier for married women with children is far larger. Among all married women ages 23-54, Table 4 shows a gap of 8.6 percentage points for women with children between tier 1 and tier 4 graduates, and a smaller gap of 1.9 percentage points for women without children that is not statistically significant. The difference in employment rates of women with children by tier is particularly large in the youngest cohort, with tier 4 graduates 12.6 percentage points more likely to be employed than tier 1 graduates. The labor market activity rates reported in Tables 2 and 4 do not adjust for any individual characteristics. The next section examines whether the patterns of lower labor market activity for graduates of schools in higher tiers are explained by differences in individual characteristics. VII. Labor market activity regressions To explore the role of individual characteristics in influencing labor market activity, I estimate a series of probit equations for each of the six measures of labor market activity for the full sample as well as by cohort. I begin with estimates, reported in Appendix B, that pool all 22